class_docstr: Base class for all MCMC `TransitionKernel`s.
  
  This class defines the minimal requirements to efficiently implement a Markov
  chain Monte Carlo (MCMC) transition kernel. A transition kernel returns a new
  state given some old state. It also takes (and returns) "side information"
  which may be used for debugging or optimization purposes (i.e, to "recycle"
  previously computed results).
  
  #### Example (random walk transition kernel):
  
  In this example we make isotropic Gaussian proposals of a given step size.
  
  ```python
  from tensorflow_probability.python.mcmc import kernel as kernel_base
  import tensorflow.compat.v2 as tf
  import tensorflow_probability as tfp
  
  tfd = tfp.distributions
  
  RWResult = collections.namedtuple("RWResult", 'target_log_prob')
  
  class RandomWalkProposalKernel(kernel_base.TransitionKernel):
    def __init__(self, target_log_prob_fn, step_size):
      self._parameters = dict(
        target_log_prob_fn = target_log_prob_fn,
        step_size = step_size)
  
    @property
    def target_log_prob_fn(self):
      return self._parameters['target_log_prob_fn']
  
    @property
    def step_size(self):
      return self._parameters['step_size']
  
    @property
    def is_calibrated(self):
      return False
  
    def one_step(self, current_state, previous_kernel_results, seed=None):
      scale = tf.broadcast_to(self.step_size, tf.shape(current_state))
      isotropic_normal = tfd.Normal(loc=current_state, scale=scale)
  
      next_state = isotropic_normal.sample(seed=seed)
      next_target_log_prob = self.target_log_prob_fn(next_state)
      new_kernel_results = previous_kernel_results._replace(
        target_log_prob = next_target_log_prob)
  
      return next_state, new_kernel_results
  
    def bootstrap_results(self, init_state):
      kernel_results = RWResult(
        target_log_prob = self.target_log_prob_fn(init_state))
      return kernel_results
  ```
docstr: Returns an object with the same type as returned by `one_step(...)[1]`.
  
  Args:
    init_state: `Tensor` or Python `list` of `Tensor`s representing the
      initial state(s) of the Markov chain(s).
  
  Returns:
    kernel_results: A (possibly nested) `tuple`, `namedtuple` or `list` of
      `Tensor`s representing internal calculations made within this function.
docstr_tok: ["Returns","an","object","with","the","same","type","as","returned","by","`","one_step","(","...",")","[","1","]","`",".","Args",":","init_state",":","`","Tensor","`","or","Python","`","list","`","of","`","Tensor","`","s","representing","the","initial","state","(","s",")","of","the","Markov","chain","(","s",")",".","Returns",":","kernel_results",":","A","(","possibly","nested",")","`","tuple","`",",","`","namedtuple","`","or","`","list","`","of","`","Tensor","`","s","representing","internal","calculations","made","within","this","function","."]
code: def bootstrap_results(
      self, init_state
  ):  # pylint: disable=unused-argument
      """Returns an object with the same type as returned by `one_step(...)[1]`.
  
      Args:
        init_state: `Tensor` or Python `list` of `Tensor`s representing the
          initial state(s) of the Markov chain(s).
  
      Returns:
        kernel_results: A (possibly nested) `tuple`, `namedtuple` or `list` of
          `Tensor`s representing internal calculations made within this function.
      """
      return []
  
file_path: probability/tensorflow_probability/python/mcmc/kernel.py
imports: import abc
  import six
label_desc: Use image node_id 3 for calling the TransitionKernel obj's underlying member method code with example usage: obj.bootstrap_results(init_state) and returns: list